Skill Gap Analysis

Code
import pandas as pd
df= pd.read_csv("data/eda_data.csv")

Group 11 Skill

Code
import pandas as pd

skills_data = {
    "Name": ["Binderiya", "Pranjul", "Pratham", "Panyang"],
    "Python": [4, 4, 5, 3],
    "SQL": [4, 4, 5, 4],
    "Machine Learning": [2, 3, 2, 2],
    "PySpark": [3, 3, 3, 3],
    "Excel": [4, 5, 5, 4],
    "Data Visualization": [5, 5, 3, 3],
    "Power Bi/ Tableau": [4, 5, 3, 4],
    "Version Control Git": [4, 4, 3, 3],
    "ETL/Data pipeline": [3, 2, 1, 2],
    "Communication": [4, 4, 5, 3],
    "Project Management": [5, 5, 5, 3],
    "Cloud Computing": [4, 4, 2, 2]
}

df_skills = pd.DataFrame(skills_data)
df_skills.set_index("Name", inplace=True)
df_skills
Python SQL Machine Learning PySpark Excel Data Visualization Power Bi/ Tableau Version Control Git ETL/Data pipeline Communication Project Management Cloud Computing
Name
Binderiya 4 4 2 3 4 5 4 4 3 4 5 4
Pranjul 4 4 3 3 5 5 5 4 2 4 5 4
Pratham 5 5 2 3 5 3 3 3 1 5 5 2
Panyang 3 4 2 3 4 3 4 3 2 3 3 2
Code
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(7, 4))
sns.heatmap(df_skills, annot=True, cmap="YlGnBu", linewidths=0.5)
plt.title("Team Skill Levels Heatmap")
plt.show()

Code
import plotly.graph_objects as go
from IPython.display import IFrame
fig = go.Figure()

for name in df_skills.index:
    values = df_skills.loc[name].tolist()
    values += values[:1]  # close the loop
    fig.add_trace(go.Scatterpolar(
        r=values,
        theta=df_skills.columns.tolist() + [df_skills.columns[0]],
        fill='toself',
        name=name
    ))

fig.update_layout(
    polar=dict(radialaxis=dict(visible=True, range=[0, 5])),
    showlegend=True,
    title='Team Skills Radar Chart'
)
fig.write_html("figures/skills_radar_chart.html")
IFrame(src="figures/skills_radar_chart.html", width='100%', height=500)
fig.show()
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Interactive Radar Chart

From this radar chart visualization we can see that our team has a lot of room for improvement for skills like PySpark and Machine Learning. Also we can see that not a lot of our team mates are confident in their skills in Cloud Computing and ETL.

Top Skills

Code
keywords = ['Data Analyst', 'Business Analyst', 'Data Engineering', 'Deep Learning',
            'Data Science', 'Data Analysis','Data Analytics',  'Market Research Analyst' 
            'LLM', 'Language Model', 'NLP', 'Natural Language Processing',
            'Computer Vision', 'Business Intelligence Analyst', 'Quantitative Analyst', 'Operations Analyst']

match = lambda col: df[col].str.contains('|'.join(keywords), case=False, na=False)

df['DATA_ANALYST_JOB'] = match('TITLE_NAME') \
             | match('SKILLS_NAME') \
             | match('SPECIALIZED_SKILLS_NAME') 
df['DATA_ANALYST_JOB'].value_counts()
DATA_ANALYST_JOB
False    37043
True     32155
Name: count, dtype: int64
Code
import ast
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px

# Safely apply literal_eval only to non-null values
df['SKILLS'] = df['SKILLS_NAME'].apply(lambda x: ast.literal_eval(x) if pd.notnull(x) else [])


data_skills = df[df['DATA_ANALYST_JOB']]['SKILLS'].explode().value_counts().reset_index()
data_skills.columns = ['Skill', 'Count']

fig = px.bar(data_skills, x='Skill', y='Count',
             title="Top Skills",
             labels={'Skill': 'Skill Name', 'Count': 'Frequency'},
             color='Skill')
df_skills.index = df_skills.index.str.strip()
Code
from collections import defaultdict

# Lowercase everything
team_skills = [s.lower().strip() for s in df_skills.columns]
job_demand_raw = data_skills.copy()
job_demand_raw['Skill'] = job_demand_raw['Skill'].str.lower().str.strip()

# New dict to map cleaned team skill to total count from job postings
skill_demand_map = defaultdict(int)

for _, row in job_demand_raw.iterrows():
    skill_in_posting = row['Skill']
    count = row['Count']
    for team_skill in team_skills:
        if team_skill in skill_in_posting:
            skill_demand_map[team_skill] += count
Code
team_skills = [s.strip().lower() for s in df_skills.columns]
print("Team skills:", team_skills)
print(job_demand_raw['Skill'].head(10).tolist())
for skill_text in job_demand_raw['Skill'].head(10):
    for team_skill in team_skills:
        if team_skill in skill_text:
            print(f" '{team_skill}' found in: '{skill_text}'")
Team skills: ['python', 'sql', 'machine learning', 'pyspark', 'excel', 'data visualization', 'power bi/ tableau', 'version control git', 'etl/data pipeline', 'communication', 'project management', 'cloud computing']
['data analysis', 'sql (programming language)', 'communication', 'management', 'python (programming language)', 'tableau (business intelligence software)', 'dashboard', 'computer science', 'problem solving', 'power bi']
 'sql' found in: 'sql (programming language)'
 'communication' found in: 'communication'
 'python' found in: 'python (programming language)'
Code
for _, row in job_demand_raw.iterrows():
    skill_text = row['Skill']
    count = row['Count']
    for team_skill in team_skills:
        if team_skill in skill_text:  # no regex, just substring
            skill_demand_map[team_skill] += count

job_demand = pd.Series(skill_demand_map)
print(job_demand)
sql                   47368
communication         47728
python                21852
excel                 18682
data visualization    14568
project management    14568
machine learning       8386
cloud computing        2390
pyspark                1008
dtype: int64
Code
job_demand = pd.Series(skill_demand_map)
job_demand.name = "Count"
team_avg = df_skills.mean()
team_avg.index = team_avg.index.str.strip().str.lower() 
# Now match only overlapping skills
common_skills = job_demand.index.intersection(team_avg.index)
team_avg = team_avg[common_skills]
job_demand = job_demand[common_skills]

# Normalize job demand
job_demand_normalized = 5 * (job_demand / job_demand.max())
job_demand_normalized.name = "Job Demand (Normalized)"

# Combine
comparison_df = pd.concat([team_avg, job_demand_normalized], axis=1)
comparison_df.columns = ["Team Average Skill", "Job Demand (Normalized)"]
comparison_df["Skill Gap"] = comparison_df["Job Demand (Normalized)"] - comparison_df["Team Average Skill"]
comparison_df.sort_values("Skill Gap", ascending=False, inplace=True)

comparison_df
Team Average Skill Job Demand (Normalized) Skill Gap
communication 4.00 5.000000 1.000000
sql 4.25 4.962286 0.712286
machine learning 2.25 0.878520 -1.371480
python 4.00 2.289222 -1.710778
data visualization 4.00 1.526148 -2.473852
excel 4.50 1.957132 -2.542868
cloud computing 3.00 0.250377 -2.749623
pyspark 3.00 0.105598 -2.894402
project management 4.50 1.526148 -2.973852
Code
comparison_df = comparison_df.reset_index().rename(columns={"index": "Skill"})
Code
import plotly.express as px

fig = px.bar(
    comparison_df,
    x='Skill',
    y='Skill Gap',
    color='Skill Gap',
    color_continuous_scale='RdBu_r',
    title='Skill Gaps: Job Market Expectations vs. Team Capability',
    labels={'Skill Gap': 'Gap (Job Demand - Team Skill)', 'Skill': 'Skill'},
)

fig.add_hline(y=0, line_dash='dash')
fig.update_layout(
    xaxis_tickangle=-45,
    yaxis_title='Gap (Positive = Market expects more)',
    font=dict(size=13),
    height=500,
    plot_bgcolor='white',
)
fig.write_html("figures/skill_gap_chart.html")
fig.show()
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This bar chart compares our team’s average proficiency in key data-related skills against job market expectations. Skills with positive values (like communication and SQL) indicate areas where market demand exceeds our current capabilities. On the other hand, negative values highlight areas where the team is ahead or closely aligned with market needs. Notably, skills like Python, cloud computing, and project management show the largest gaps, suggesting priority areas for upskilling.